

<!DOCTYPE html>
<!--[if IE 8]><html class="no-js lt-ie9" lang="en" > <![endif]-->
<!--[if gt IE 8]><!--> <html class="no-js" lang="en" > <!--<![endif]-->
<head>
  <meta charset="utf-8">
  
  <meta name="viewport" content="width=device-width, initial-scale=1.0">
  <meta name="Description" content="scikit-learn: machine learning in Python">

  
  <title>sklearn.cluster.dbscan &mdash; scikit-learn 0.22 documentation</title>
  
  <link rel="canonical" href="http://scikit-learn.org/stable/modules/generated/sklearn.cluster.dbscan.html" />

  
  <link rel="shortcut icon" href="../../_static/favicon.ico"/>
  

  <link rel="stylesheet" href="../../_static/css/vendor/bootstrap.min.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/gallery.css" type="text/css" />
  <link rel="stylesheet" href="../../_static/css/theme.css" type="text/css" />
<script id="documentation_options" data-url_root="../../" src="../../_static/documentation_options.js"></script>
<script src="../../_static/jquery.js"></script> 
</head>
<body>
<nav id="navbar" class="sk-docs-navbar navbar navbar-expand-md navbar-light bg-light py-0">
  <div class="container-fluid sk-docs-container px-0">
      <a class="navbar-brand py-0" href="../../index.html">
        <img
          class="sk-brand-img"
          src="../../_static/scikit-learn-logo-small.png"
          alt="logo"/>
      </a>
    <button
      id="sk-navbar-toggler"
      class="navbar-toggler"
      type="button"
      data-toggle="collapse"
      data-target="#navbarSupportedContent"
      aria-controls="navbarSupportedContent"
      aria-expanded="false"
      aria-label="Toggle navigation"
    >
      <span class="navbar-toggler-icon"></span>
    </button>

    <div class="sk-navbar-collapse collapse navbar-collapse" id="navbarSupportedContent">
      <ul class="navbar-nav mr-auto">
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../install.html">Install</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../user_guide.html">User Guide</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../classes.html">API</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link" href="../../auto_examples/index.html">Examples</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../getting_started.html">Getting Started</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../tutorial/index.html">Tutorial</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../glossary.html">Glossary</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../developers/index.html">Development</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../faq.html">FAQ</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../related_projects.html">Related packages</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../roadmap.html">Roadmap</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="../../about.html">About us</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
        </li>
        <li class="nav-item">
          <a class="sk-nav-link nav-link nav-more-item-mobile-items" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
        </li>
        <li class="nav-item dropdown nav-more-item-dropdown">
          <a class="sk-nav-link nav-link dropdown-toggle" href="#" id="navbarDropdown" role="button" data-toggle="dropdown" aria-haspopup="true" aria-expanded="false">More</a>
          <div class="dropdown-menu" aria-labelledby="navbarDropdown">
              <a class="sk-nav-dropdown-item dropdown-item" href="../../getting_started.html">Getting Started</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../tutorial/index.html">Tutorial</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../glossary.html">Glossary</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../developers/index.html">Development</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../faq.html">FAQ</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../related_projects.html">Related packages</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../roadmap.html">Roadmap</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="../../about.html">About us</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://github.com/scikit-learn/scikit-learn">GitHub</a>
              <a class="sk-nav-dropdown-item dropdown-item" href="https://scikit-learn.org/dev/versions.html">Other Versions</a>
          </div>
        </li>
      </ul>
      <div id="searchbox" role="search">
          <div class="searchformwrapper">
          <form class="search" action="../../search.html" method="get">
            <input class="sk-search-text-input" type="text" name="q" aria-labelledby="searchlabel" />
            <input class="sk-search-text-btn" type="submit" value="Go" />
          </form>
          </div>
      </div>
    </div>
  </div>
</nav>
<div class="d-flex" id="sk-doc-wrapper">
    <input type="checkbox" name="sk-toggle-checkbox" id="sk-toggle-checkbox">
    <label id="sk-sidemenu-toggle" class="sk-btn-toggle-toc btn sk-btn-primary" for="sk-toggle-checkbox">Toggle Menu</label>
    <div id="sk-sidebar-wrapper" class="border-right">
      <div class="sk-sidebar-toc-wrapper">
        <div class="sk-sidebar-toc-logo">
          <a href="../../index.html">
            <img
              class="sk-brand-img"
              src="../../_static/scikit-learn-logo-small.png"
              alt="logo"/>
          </a>
        </div>
        <div class="btn-group w-100 mb-2" role="group" aria-label="rellinks">
            <a href="sklearn.cluster.compute_optics_graph.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.cluster.compute_optics_graph">Prev</a><a href="../classes.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="API Reference">Up</a>
            <a href="sklearn.cluster.estimate_bandwidth.html" role="button" class="btn sk-btn-rellink py-1" sk-rellink-tooltip="sklearn.cluster.estimate_bandwidth">Next</a>
        </div>
        <div class="alert alert-danger p-1 mb-2" role="alert">
          <p class="text-center mb-0">
          <strong>scikit-learn 0.22</strong><br/>
          <a href="http://scikit-learn.org/dev/versions.html">Other versions</a>
          </p>
        </div>
        <div class="alert alert-warning p-1 mb-2" role="alert">
          <p class="text-center mb-0">
            Please <a class="font-weight-bold" href="../../about.html#citing-scikit-learn"><string>cite us</string></a> if you use the software.
          </p>
        </div>
          <div class="sk-sidebar-toc">
            <ul>
<li><a class="reference internal" href="#"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code>.dbscan</a></li>
</ul>

          </div>
      </div>
    </div>
    <div id="sk-page-content-wrapper">
      <div class="sk-page-content container-fluid body px-md-3" role="main">
        
  <div class="section" id="sklearn-cluster-dbscan">
<h1><a class="reference internal" href="../classes.html#module-sklearn.cluster" title="sklearn.cluster"><code class="xref py py-mod docutils literal notranslate"><span class="pre">sklearn.cluster</span></code></a>.dbscan<a class="headerlink" href="#sklearn-cluster-dbscan" title="Permalink to this headline">¶</a></h1>
<dl class="function">
<dt id="sklearn.cluster.dbscan">
<code class="sig-prename descclassname">sklearn.cluster.</code><code class="sig-name descname">dbscan</code><span class="sig-paren">(</span><em class="sig-param">X</em>, <em class="sig-param">eps=0.5</em>, <em class="sig-param">min_samples=5</em>, <em class="sig-param">metric='minkowski'</em>, <em class="sig-param">metric_params=None</em>, <em class="sig-param">algorithm='auto'</em>, <em class="sig-param">leaf_size=30</em>, <em class="sig-param">p=2</em>, <em class="sig-param">sample_weight=None</em>, <em class="sig-param">n_jobs=None</em><span class="sig-paren">)</span><a class="reference external" href="https://github.com/scikit-learn/scikit-learn/blob/5f3c3f037/sklearn/cluster/_dbscan.py#L24"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#sklearn.cluster.dbscan" title="Permalink to this definition">¶</a></dt>
<dd><p>Perform DBSCAN clustering from vector array or distance matrix.</p>
<p>Read more in the <a class="reference internal" href="../clustering.html#dbscan"><span class="std std-ref">User Guide</span></a>.</p>
<dl class="field-list">
<dt class="field-odd">Parameters</dt>
<dd class="field-odd"><dl>
<dt><strong>X</strong><span class="classifier">array or sparse (CSR) matrix of shape (n_samples, n_features), or             array of shape (n_samples, n_samples)</span></dt><dd><p>A feature array, or array of distances between samples if
<code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code>.</p>
</dd>
<dt><strong>eps</strong><span class="classifier">float, optional</span></dt><dd><p>The maximum distance between two samples for one to be considered
as in the neighborhood of the other. This is not a maximum bound
on the distances of points within a cluster. This is the most
important DBSCAN parameter to choose appropriately for your data set
and distance function.</p>
</dd>
<dt><strong>min_samples</strong><span class="classifier">int, optional</span></dt><dd><p>The number of samples (or total weight) in a neighborhood for a point
to be considered as a core point. This includes the point itself.</p>
</dd>
<dt><strong>metric</strong><span class="classifier">string, or callable</span></dt><dd><p>The metric to use when calculating distance between instances in a
feature array. If metric is a string or callable, it must be one of
the options allowed by <a class="reference internal" href="sklearn.metrics.pairwise_distances.html#sklearn.metrics.pairwise_distances" title="sklearn.metrics.pairwise_distances"><code class="xref py py-func docutils literal notranslate"><span class="pre">sklearn.metrics.pairwise_distances</span></code></a> for
its metric parameter.
If metric is “precomputed”, X is assumed to be a distance matrix and
must be square during fit. X may be a <a class="reference internal" href="../../glossary.html#term-sparse-graph"><span class="xref std std-term">Glossary</span></a>,
in which case only “nonzero” elements may be considered neighbors.</p>
</dd>
<dt><strong>metric_params</strong><span class="classifier">dict, optional</span></dt><dd><p>Additional keyword arguments for the metric function.</p>
<div class="versionadded">
<p><span class="versionmodified added">New in version 0.19.</span></p>
</div>
</dd>
<dt><strong>algorithm</strong><span class="classifier">{‘auto’, ‘ball_tree’, ‘kd_tree’, ‘brute’}, optional</span></dt><dd><p>The algorithm to be used by the NearestNeighbors module
to compute pointwise distances and find nearest neighbors.
See NearestNeighbors module documentation for details.</p>
</dd>
<dt><strong>leaf_size</strong><span class="classifier">int, optional (default = 30)</span></dt><dd><p>Leaf size passed to BallTree or cKDTree. This can affect the speed
of the construction and query, as well as the memory required
to store the tree. The optimal value depends
on the nature of the problem.</p>
</dd>
<dt><strong>p</strong><span class="classifier">float, optional</span></dt><dd><p>The power of the Minkowski metric to be used to calculate distance
between points.</p>
</dd>
<dt><strong>sample_weight</strong><span class="classifier">array, shape (n_samples,), optional</span></dt><dd><p>Weight of each sample, such that a sample with a weight of at least
<code class="docutils literal notranslate"><span class="pre">min_samples</span></code> is by itself a core sample; a sample with negative
weight may inhibit its eps-neighbor from being core.
Note that weights are absolute, and default to 1.</p>
</dd>
<dt><strong>n_jobs</strong><span class="classifier">int or None, optional (default=None)</span></dt><dd><p>The number of parallel jobs to run for neighbors search.
<code class="docutils literal notranslate"><span class="pre">None</span></code> means 1 unless in a <a class="reference external" href="https://joblib.readthedocs.io/en/latest/parallel.html#joblib.parallel_backend" title="(in joblib v0.14.1.dev0)"><code class="xref py py-obj docutils literal notranslate"><span class="pre">joblib.parallel_backend</span></code></a> context.
<code class="docutils literal notranslate"><span class="pre">-1</span></code> means using all processors. See <a class="reference internal" href="../../glossary.html#term-n-jobs"><span class="xref std std-term">Glossary</span></a>
for more details.</p>
</dd>
</dl>
</dd>
<dt class="field-even">Returns</dt>
<dd class="field-even"><dl class="simple">
<dt><strong>core_samples</strong><span class="classifier">array [n_core_samples]</span></dt><dd><p>Indices of core samples.</p>
</dd>
<dt><strong>labels</strong><span class="classifier">array [n_samples]</span></dt><dd><p>Cluster labels for each point.  Noisy samples are given the label -1.</p>
</dd>
</dl>
</dd>
</dl>
<div class="admonition seealso">
<p class="admonition-title">See also</p>
<dl class="simple">
<dt><a class="reference internal" href="sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN" title="sklearn.cluster.DBSCAN"><code class="xref py py-obj docutils literal notranslate"><span class="pre">DBSCAN</span></code></a></dt><dd><p>An estimator interface for this clustering algorithm.</p>
</dd>
<dt><a class="reference internal" href="sklearn.cluster.OPTICS.html#sklearn.cluster.OPTICS" title="sklearn.cluster.OPTICS"><code class="xref py py-obj docutils literal notranslate"><span class="pre">OPTICS</span></code></a></dt><dd><p>A similar estimator interface clustering at multiple values of eps. Our implementation is optimized for memory usage.</p>
</dd>
</dl>
</div>
<p class="rubric">Notes</p>
<p>For an example, see <a class="reference internal" href="../../auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py"><span class="std std-ref">examples/cluster/plot_dbscan.py</span></a>.</p>
<p>This implementation bulk-computes all neighborhood queries, which increases
the memory complexity to O(n.d) where d is the average number of neighbors,
while original DBSCAN had memory complexity O(n). It may attract a higher
memory complexity when querying these nearest neighborhoods, depending
on the <code class="docutils literal notranslate"><span class="pre">algorithm</span></code>.</p>
<p>One way to avoid the query complexity is to pre-compute sparse
neighborhoods in chunks using
<a class="reference internal" href="sklearn.neighbors.NearestNeighbors.html#sklearn.neighbors.NearestNeighbors.radius_neighbors_graph" title="sklearn.neighbors.NearestNeighbors.radius_neighbors_graph"><code class="xref py py-func docutils literal notranslate"><span class="pre">NearestNeighbors.radius_neighbors_graph</span></code></a> with
<code class="docutils literal notranslate"><span class="pre">mode='distance'</span></code>, then using <code class="docutils literal notranslate"><span class="pre">metric='precomputed'</span></code> here.</p>
<p>Another way to reduce memory and computation time is to remove
(near-)duplicate points and use <code class="docutils literal notranslate"><span class="pre">sample_weight</span></code> instead.</p>
<p><code class="xref py py-func docutils literal notranslate"><span class="pre">cluster.optics</span></code> provides a similar
clustering with lower memory usage.</p>
<p class="rubric">References</p>
<p>Ester, M., H. P. Kriegel, J. Sander, and X. Xu, “A Density-Based
Algorithm for Discovering Clusters in Large Spatial Databases with Noise”.
In: Proceedings of the 2nd International Conference on Knowledge Discovery
and Data Mining, Portland, OR, AAAI Press, pp. 226-231. 1996</p>
<p>Schubert, E., Sander, J., Ester, M., Kriegel, H. P., &amp; Xu, X. (2017).
DBSCAN revisited, revisited: why and how you should (still) use DBSCAN.
ACM Transactions on Database Systems (TODS), 42(3), 19.</p>
</dd></dl>

<div class="clearer"></div></div>


      </div>
    <div class="container">
      <footer class="sk-content-footer">
            &copy; 2007 - 2019, scikit-learn developers (BSD License).
          <a href="../../_sources/modules/generated/sklearn.cluster.dbscan.rst.txt" rel="nofollow">Show this page source</a>
      </footer>
    </div>
  </div>
</div>
<script src="../../_static/js/vendor/bootstrap.min.js"></script>

<script>
    window.ga=window.ga||function(){(ga.q=ga.q||[]).push(arguments)};ga.l=+new Date;
    ga('create', 'UA-22606712-2', 'auto');
    ga('set', 'anonymizeIp', true);
    ga('send', 'pageview');
</script>
<script async src='https://www.google-analytics.com/analytics.js'></script>


<script>
$(document).ready(function() {
    /* Add a [>>>] button on the top-right corner of code samples to hide
     * the >>> and ... prompts and the output and thus make the code
     * copyable. */
    var div = $('.highlight-python .highlight,' +
                '.highlight-python3 .highlight,' +
                '.highlight-pycon .highlight,' +
		'.highlight-default .highlight')
    var pre = div.find('pre');

    // get the styles from the current theme
    pre.parent().parent().css('position', 'relative');
    var hide_text = 'Hide prompts and outputs';
    var show_text = 'Show prompts and outputs';

    // create and add the button to all the code blocks that contain >>>
    div.each(function(index) {
        var jthis = $(this);
        if (jthis.find('.gp').length > 0) {
            var button = $('<span class="copybutton">&gt;&gt;&gt;</span>');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
            jthis.prepend(button);
        }
        // tracebacks (.gt) contain bare text elements that need to be
        // wrapped in a span to work with .nextUntil() (see later)
        jthis.find('pre:has(.gt)').contents().filter(function() {
            return ((this.nodeType == 3) && (this.data.trim().length > 0));
        }).wrap('<span>');
    });

    // define the behavior of the button when it's clicked
    $('.copybutton').click(function(e){
        e.preventDefault();
        var button = $(this);
        if (button.data('hidden') === 'false') {
            // hide the code output
            button.parent().find('.go, .gp, .gt').hide();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'hidden');
            button.css('text-decoration', 'line-through');
            button.attr('title', show_text);
            button.data('hidden', 'true');
        } else {
            // show the code output
            button.parent().find('.go, .gp, .gt').show();
            button.next('pre').find('.gt').nextUntil('.gp, .go').css('visibility', 'visible');
            button.css('text-decoration', 'none');
            button.attr('title', hide_text);
            button.data('hidden', 'false');
        }
    });

	/*** Add permalink buttons next to glossary terms ***/
	$('dl.glossary > dt[id]').append(function() {
		return ('<a class="headerlink" href="#' +
			    this.getAttribute('id') +
			    '" title="Permalink to this term">¶</a>');
	});
  /*** Hide navbar when scrolling down ***/
  // Returns true when headerlink target matches hash in url
  (function() {
    hashTargetOnTop = function() {
        var hash = window.location.hash;
        if ( hash.length < 2 ) { return false; }

        var target = document.getElementById( hash.slice(1) );
        if ( target === null ) { return false; }

        var top = target.getBoundingClientRect().top;
        return (top < 2) && (top > -2);
    };

    // Hide navbar on load if hash target is on top
    var navBar = document.getElementById("navbar");
    var navBarToggler = document.getElementById("sk-navbar-toggler");
    var navBarHeightHidden = "-" + navBar.getBoundingClientRect().height + "px";
    var $window = $(window);

    hideNavBar = function() {
        navBar.style.top = navBarHeightHidden;
    };

    showNavBar = function() {
        navBar.style.top = "0";
    }

    if (hashTargetOnTop()) {
        hideNavBar()
    }

    var prevScrollpos = window.pageYOffset;
    hideOnScroll = function(lastScrollTop) {
        if (($window.width() < 768) && (navBarToggler.getAttribute("aria-expanded") === 'true')) {
            return;
        }
        if (lastScrollTop > 2 && (prevScrollpos <= lastScrollTop) || hashTargetOnTop()){
            hideNavBar()
        } else {
            showNavBar()
        }
        prevScrollpos = lastScrollTop;
    };

    /*** high preformance scroll event listener***/
    var raf = window.requestAnimationFrame ||
        window.webkitRequestAnimationFrame ||
        window.mozRequestAnimationFrame ||
        window.msRequestAnimationFrame ||
        window.oRequestAnimationFrame;
    var lastScrollTop = $window.scrollTop();

    if (raf) {
        loop();
    }

    function loop() {
        var scrollTop = $window.scrollTop();
        if (lastScrollTop === scrollTop) {
            raf(loop);
            return;
        } else {
            lastScrollTop = scrollTop;
            hideOnScroll(lastScrollTop);
            raf(loop);
        }
    }
  })();
});

</script>
    
<script id="MathJax-script" async src="https://cdn.jsdelivr.net/npm/mathjax@3/es5/tex-chtml.js"></script>
    
</body>
</html>